The impact of low-cost molecular geometry optimization in property prediction via graph neural network (2022)
- Authors:
- Autor USP: SILVA, JUAREZ LOPES FERREIRA DA - IQSC
- Unidade: IQSC
- DOI: 10.1109/ICMLA55696.2022.00092
- Subjects: ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA); REDES NEURAIS; ALGORITMOS
- Keywords: property prediction; geometry optimization
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Book of Abstracts
- Volume/Número/Paginação/Ano: p. 603-608, 2022
- Conference titles: IEEE International Conference on Machine Learning and Applications (ICMLA)
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
PINHEIRO, Gabriel A et al. The impact of low-cost molecular geometry optimization in property prediction via graph neural network. 2022, Anais.. Nassau: Instituto de Química de São Carlos, Universidade de São Paulo, 2022. p. 603-608. Disponível em: https://doi.org/10.1109/ICMLA55696.2022.00092. Acesso em: 30 abr. 2024. -
APA
Pinheiro, G. A., Calderan, F. V., Silva, J. L. F. da, & Quiles, M. G. (2022). The impact of low-cost molecular geometry optimization in property prediction via graph neural network. In Book of Abstracts (p. 603-608). Nassau: Instituto de Química de São Carlos, Universidade de São Paulo. doi:10.1109/ICMLA55696.2022.00092 -
NLM
Pinheiro GA, Calderan FV, Silva JLF da, Quiles MG. The impact of low-cost molecular geometry optimization in property prediction via graph neural network [Internet]. Book of Abstracts. 2022 ; 603-608.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1109/ICMLA55696.2022.00092 -
Vancouver
Pinheiro GA, Calderan FV, Silva JLF da, Quiles MG. The impact of low-cost molecular geometry optimization in property prediction via graph neural network [Internet]. Book of Abstracts. 2022 ; 603-608.[citado 2024 abr. 30 ] Available from: https://doi.org/10.1109/ICMLA55696.2022.00092 - Hybrid density functional study of small Rhn (n = 2−15) clusters
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Informações sobre o DOI: 10.1109/ICMLA55696.2022.00092 (Fonte: oaDOI API)
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